Technology

The world’s fastest supercomputer identified chemicals that could stop coronavirus from spreading

The novel coronavirus presents an unprecedented challenge for scientists: The speed at which the virus spreads means they must accelerate their research.

But this is what the world’s fastest supercomputer was built for.
Summit, IBM’s supercomputer equipped with the “brain of AI,” ran thousands of simulations to analyze which drug compounds might effectively stop the virus from infecting host cells.
The supercomputer identified 77 of them. It’s a promising step toward creating the most effective vaccine.
Researchers at Oak Ridge National Laboratory published their findings in the journal ChemRxiv.

Summit was commissioned by the US Department of Energy in 2014 for the purpose it’s serving now — solving the world’s problems.
It’s got the power of 200 petaflops, which means it has the computing speed of 200 quadrillion calculations per second, aka: It’s 1 million times more powerful than the fastest laptop.

Summit, the world’s most powerful supercomputer, modeled how different drug compounds might prevent the coronavirus from spreading to other cells.
At its station in Oak Ridge National Laboratory in Tennessee, Summit has identified patterns in cellular systems that precede Alzheimer’s, analyzed genes that contribute to traits like opioid addiction and predicted extreme weather based on climate simulations.

Viruses infect host cells by injecting them with a “spike” of genetic material. Summit’s job is to find drug compounds that could bind to that spike and potentially stop the spread.

Oak Ridge researcher Micholas Smith created a model of the coronavirus spike based on research published in January. With Summit, he simulated how the atoms and particles in the viral protein would react to different compounds.
The supercomputer ran simulations of over 8,000 compounds that could bind to the spike protein of the virus, which could limit its ability to spread to host cells. Summit identified 77 of them and ranked them based on how likely they were to bind to the spike.

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